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Found 6 papers, 1 papers with code
Multi-Person 3D Human Pose Estimation from Monocular Images
Multi-person 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data.
Ranked #8 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Human Pose Estimation
3D Multi-Person Human Pose Estimation
ProtoGAN: Towards Few Shot Learning for Action Recognition
To the best of our knowledge, we are the first to report the results for G-FSL and provide a strong benchmark for future research.
Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation
The best performing methods for 3D human pose estimation from monocular images require large amounts of in-the-wild 2D and controlled 3D pose annotated datasets which are costly and require sophisticated systems to acquire.
An Improved Learning Framework for Covariant Local Feature Detection
However, just learning from covariant constraint can lead to detection of unstable features.
A Large Dataset for Improving Patch Matching
Similarly on the Strecha dataset, we see an improvement of 3-5% for the matching task in non-planar scenes.
Improved Descriptors for Patch Matching and Reconstruction
Scenes from the Oxford ACRD, MVS and Synthetic datasets are used for evaluating the patch matching performance of the learnt descriptors while the Strecha dataset is used to evaluate the 3D reconstruction task.
